Hybrid observation technique of hilal (HOTOH) 2.0: the implementation of image processing technique for hilal visibility detection using python

This product introduces a novel hybrid observation technique called HOTOH 2.0 (Hybrid Observation Technique of Hilal 2.0) designed to detect the Hilal (new crescent moon). The product focuses on leveraging image processing techniques using the Python programming language to enhance the visibility and accuracy of Hilal detection. The methodology of HOTOH 2.0 comprises three key steps. Firstly, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to improve the contrast of the image, making subtle details more visible and aiding in the subsequent analysis. Secondly, Bilateral Filter (BF) is employed to reduce noise present in the image, ensuring a cleaner and more accurate detection process. Thirdly, Maximally Stable Extremal Regions (MSER) and Hough Circle Transform (CHT) is utilized to identify Hilal and the circular shape of the of it within the processed image. Finaly, PyEphem was used to calculate the width of the Hilal after the detection method was used. This technique significantly enhances the efficiency and reliability of Hilal observation, especially in challenging atmospheric conditions or when observing faint crescents. The impact of HOTOH 2.0 extends to facilitating more precise Islamic calendar calculations and aiding in the determination of important religious events. In conclusion, the Hybrid Observation Technique of Hilal (HOTOH) 2.0 represents a valuable advancement in the field of Hilal visibility detection, offering a comprehensive and effective approach through the integration of image processing methodologies. This product contributes to the development of accurate astronomical observations with practical applications in various domains, including religious practices and calendar calculationsAhmad Lutfi Afifi Mohd Nasir